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April 4, 2014Data Mining: Concepts and Techniques

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Data Mining: Concepts and Techniques

©Jiawei Han and Micheline Kamber

Intelligent Database Systems Research Lab

School of Computing Science

Simon Fraser University, Canada

http://www.cs.sfu.ca



Chapter 10: Applications and Trends in Data Mining

Data mining applications

Data mining system products and research

prototypes

Additional themes on data mining

Social impact of data mining

Trends in data mining

Summary



Data Mining Applications

Data mining is a young discipline with wide and diverse applications

There is still a nontrivial gap between general principles of data mining and domain-specific, effective data mining tools for particular applications

Some application domains (covered in this chapter)

Biomedical and DNA data analysis

Financial data analysis

Retail industry

Telecommunication industry



Biomedical Data Mining and DNA Analysis

DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T).

Gene: a sequence of hundreds of individual nucleotides arranged in a particular order

Humans have around 100,000 genes

Tremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genes

Semantic integration of heterogeneous, distributed genome databases

Current: highly distributed, uncontrolled generation and use of a wide variety of DNA data

Data cleaning and data integration methods developed in data mining will help



DNA Analysis: Examples

Similarity search and comparison among DNA sequences

Compare the frequently occurring patterns of each class (e.g., diseased and healthy)

Identify gene sequence patterns that play roles in various diseases

Association analysis: identification of co-occurring gene sequences

Most diseases are not triggered by a single gene but by a combination of genes acting together

Association analysis may help determine the kinds of genes that are likely to co-occur together in target samples

Path analysis: linking genes to different disease development stages

Different genes may become active at different stages of the disease

Develop pharmaceutical interventions that target the different stages separately

Visualization tools and genetic data analysis



Data Mining for Financial Data Analysis

Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high quality

Design and construction of data warehouses for multidimensional data analysis and data mining

View the debt and revenue changes by month, by region, by sector, and by other factors

Access statistical information such as max, min, total, average, trend, etc.

Loan payment prediction/consumer credit policy analysis

feature selection and attribute relevance ranking

Loan payment performance

Consumer credit rating



Financial Data Mining

Classification and clustering of customers for targeted marketing

multidimensional segmentation by nearest-neighbor, classification, decision trees, etc. to identify customer groups or associate a new customer to an appropriate customer group

Detection of money laundering and other financial crimes integration of from multiple DBs (e.g., bank

transactions, federal/state crime history DBs) Tools: data visualization, linkage analysis,

classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences)



Data Mining for Retail Industry

Retail industry: huge amounts of data on sales,

customer shopping history, etc.

Applications of retail data mining

Identify customer buying behaviors

Discover customer shopping patterns and trends

Improve the quality of customer service

Achieve better customer retention and satisfaction

Enhance goods consumption ratios

Design more effective goods transportation and

distribution policies



Data Mining in Retail Industry: Examples

Design and construction of data warehouses based on the benefits of data mining

Multidimensional analysis of sales, customers, products, time, and region

Analysis of the effectiveness of sales campaigns

Customer retention: Analysis of customer loyalty

Use customer loyalty card information to register sequences of purchases of particular customers

Use sequential pattern mining to investigate changes in customer consumption or loyalty

Suggest adjustments on the pricing and variety of goods

Purchase recommendation and cross-reference of items



Data Mining for Telecomm. Industry (1)

A rapidly expanding and highly competitive industry and

a great demand for data mining

Understand the business involved

Identify telecommunication patterns

Catch fraudulent activities

Make better use of resources

Improve the quality of service

Multidimensional analysis of telecommunication data

Intrinsically multidimensional: calling-time, duration,

location of caller, location of callee, type of call, etc.



Data Mining for Telecomm. Industry (2)

Fraudulent pattern analysis and the identification of unusual

patterns

Identify potentially fraudulent users and their atypical usage

patterns

Detect attempts to gain fraudulent entry to customer accounts

Discover unusual patterns which may need special attention

Multidimensional association and sequential pattern analysis

Find usage patterns for a set of communication services by

customer group, by month, etc.

Promote the sales of specific services

Improve the availability of particular services in a region

Use of visualization tools in telecommunication data analysis






prototypes




Summary



How to choose a data mining system?

Commercial data mining systems have little in common

Different data mining functionality or methodology

May even work with completely different kinds of data sets

Need multiple dimensional view in selection

Data types: relational, transactional, text, time sequence, spatial?

System issues

running on only one or on several operating systems?

a client/server architecture?

Provide Web-based interfaces and allow XML data as input and/or output?



How to Choose a Data Mining System? (2)

Data sources

ASCII text files, multiple relational data sources

support ODBC connections (OLE DB, JDBC)?

Data mining functions and methodologies

One vs. multiple data mining functions

One vs. variety of methods per function More data mining functions and methods per function provide

the user with greater flexibility and analysis power

Coupling with DB and/or data warehouse systems

Four forms of coupling: no coupling, loose coupling, semitight coupling, and tight coupling

Ideally, a data mining system should be tightly coupled with a database system



How to Choose a Data Mining System? (3)

Scalability Row (or database size) scalability Column (or dimension) scalability Curse of dimensionality: it is much more challenging to

make a system column scalable that row scalable Visualization tools

“A picture is worth a thousand words” Visualization categories: data visualization, mining

result visualization, mining process visualization, and visual data mining

Data mining query language and graphical user interface Easy-to-use and high-quality graphical user interface Essential for user-guided, highly interactive data

mining



Examples of Data Mining Systems (1)

IBM Intelligent Miner A wide range of data mining algorithms Scalable mining algorithms Toolkits: neural network algorithms, statistical

methods, data preparation, and data visualization tools Tight integration with IBM's DB2 relational database

system SAS Enterprise Miner

A variety of statistical analysis tools Data warehouse tools and multiple data mining

algorithms Mirosoft SQLServer 2000

Integrate DB and OLAP with mining Support OLEDB for DM standard



Examples of Data Mining Systems (2)

SGI MineSet Multiple data mining algorithms and advanced statistics Advanced visualization tools

Clementine (SPSS)

An integrated data mining development environment for end-users and developers

Multiple data mining algorithms and visualization tools

DBMiner (DBMiner Technology Inc.)

Multiple data mining modules: discovery-driven OLAP analysis, association, classification, and clustering

Efficient, association and sequential-pattern mining functions, and visual classification tool

Mining both relational databases and data warehouses






prototypes




Summary



Visual Data Mining

Visualization: use of computer graphics to create visual images which aid in the understanding of complex, often massive representations of data

Visual Data Mining: the process of discovering implicit but useful knowledge from large data sets using visualization techniques

Purpose of Visualization Gain insight into an information space by mapping data onto

graphical primitives

Provide qualitative overview of large data sets

Search for patterns, trends, structure, irregularities, relationships among data.

Help find interesting regions and suitable parameters for further quantitative analysis.

Provide a visual proof of computer representations derived



Visual Data Mining & Data Visualization

Integration of visualization and data mining

data visualization

data mining result visualization

data mining process visualization

interactive visual data mining

Data visualization

Data in a database or data warehouse can be viewed

at different levels of granularity or abstraction

as different combinations of attributes or dimensions

Data can be presented in various visual forms



Boxplots from Statsoft: multiple variable combinations



Data Mining Result Visualization

Presentation of the results or knowledge obtained from

data mining in visual forms

Examples

Scatter plots and boxplots (obtained from descriptive

data mining)

Decision trees

Association rules

Clusters

Outliers

Generalized rules



Visualization of data mining results in SAS Enterprise Miner: scatter plots



Visualization of association rules in MineSet 3.0



Visualization of a decision tree in MineSet 3.0



Visualization of cluster groupings in IBM Intelligent Miner



Data Mining Process Visualization

Presentation of the various processes of data mining in

visual forms so that users can see

How the data are extracted

From which database or data warehouse they are extracted

How the selected data are cleaned, integrated, preprocessed, and mined

Which method is selected at data mining

Where the results are stored

How they may be viewed



Visualization of Data Mining Processes by Clementine



Interactive Visual Data Mining

Using visualization tools in the data mining process to

help users make smart data mining decisions

Example

Display the data distribution in a set of attributes

using colored sectors or columns (depending on

whether the whole space is represented by either a

circle or a set of columns)

Use the display to which sector should first be

selected for classification and where a good split

point for this sector may be



Interactive Visual Mining by Perception-Based Classification (PBC)



Audio Data Mining

Uses audio signals to indicate the patterns of data or the features of data mining results

An interesting alternative to visual mining

An inverse task of mining audio (such as music) databases which is to find patterns from audio data

Visual data mining may disclose interesting patterns using graphical displays, but requires users to concentrate on watching patterns

Instead, transform patterns into sound and music and listen to pitches, rhythms, tune, and melody in order to identify anything interesting or unusual



Scientific and Statistical Data Mining (1)

There are many well-established statistical techniques for data analysis, particularly for numeric data

applied extensively to data from scientific experiments and data from economics and the social sciences

Regression

predict the value of a response (dependent) variable from one or more predictor (independent) variables where the variables are numeric

forms of regression: linear, multiple, weighted, polynomial, nonparametric, and robust

Generalized linear models

allow a categorical response variable (or some transformation of it) to be related to a set of predictor variables

similar to the modeling of a numeric response variable using linear regression

include logistic regression and Poisson regression




Regression trees Binary trees used for classification and prediction Similar to decision trees:Tests are performed at the internal nodes Difference is at the leaf level

In a decision tree a majority voting is performed to assign a class label to the leaf

In a regression tree the mean of the objective attribute is computed and used as the predicted value

Analysis of variance Analyze experimental data for two or more populations described

by a numeric response variable and one or more categorical variables (factors)

Mixed-effect models For analyzing grouped data, i.e. data that can be classified

according to one or more grouping variables Typically describe relationships between a response variable and

some covariates in data grouped according to one or more factors




Factor analysis determine which vars are combined to generate a given factor e.g., for many psychiatric data, one can indirectly measure other

quantities (such as test scores) that reflect the factor of interest Discriminant analysis

predict a categorical response variable, commonly used in social science

Attempts to determine several discriminant functions (linear combinations of the independent variables) that discriminate among the groups defined by the response variable

Time series: many methods such as autoregression, ARIMA (Autoregressive integrated moving-average modeling), long memory time-series modeling

Survival analysis predict the probability that a patient undergoing a medical

treatment would survive at least to time t (life span prediction) Quality control

display group summary charts



Theoretical Foundations of Data Mining (1)

Data reduction

The basis of data mining is to reduce the data representation

Trades accuracy for speed in response

Data compression

The basis of data mining is to compress the given data by encoding in terms of bits, association rules, decision trees, clusters, etc.

Pattern discovery

The basis of data mining is to discover patterns occurring in the database, such as associations, classification models, sequential patterns, etc.



Theoretical Foundations of Data Mining (2)

Probability theory

The basis of data mining is to discover joint probability distributions of random variables

Microeconomic view

A view of utility: the task of data mining is finding patterns that are interesting only to the extent in that they can be used in the decision-making process of some enterprise

Inductive databases

Data mining is the problem of performing inductive logic on databases,

The task is to query the data and the theory (i.e., patterns) of the database

Popular among many researchers in database systems



Data Mining and Intelligent Query Answering

Query answering

Direct query answering: returns exactly what is being asked

Intelligent (or cooperative) query answering: analyzes the intent of the query and provides generalized, neighborhood or associated information relevant to the query

Some users may not have a clear idea of exactly what to mine or what is contained in the database

Intelligent query answering analyzes the user's intent and answers queries in an intelligent way



Data Mining and Intelligent Query Answering (2)

A general framework for the integration of data mining

and intelligent query answering

Data query: finds concrete data stored in a database

Knowledge query: finds rules, patterns, and other

kinds of knowledge in a database

Ex. Three ways to improve on-line shopping service

Informative query answering by providing summary

information

Suggestion of additional items based on association

analysis

Product promotion by sequential pattern mining






prototypes




Summary



Is Data Mining a Hype or Will It Be Persistent?

Data mining is a technology

Technological life cycle

Innovators

Early adopters

Chasm

Early majority

Late majority

Laggards



Life Cycle of Technology Adoption

Data mining is at Chasm!?

Existing data mining systems are too generic

Need business-specific data mining solutions and smooth integration of business logic with data mining functions



Data Mining: Merely Managers' Business or Everyone's?

Data mining will surely be an important tool for managers’ decision making

Bill Gates: “Business @ the speed of thought”

The amount of the available data is increasing, and data mining systems will be more affordable

Multiple personal uses

Mine your family's medical history to identify genetically-related medical conditions

Mine the records of the companies you deal with

Mine data on stocks and company performance, etc.

Invisible data mining

Build data mining functions into many intelligent tools



Social Impacts: Threat to Privacy and Data Security?

Is data mining a threat to privacy and data security?

“Big Brother”, “Big Banker”, and “Big Business” are carefully watching you

Profiling information is collected every time You use your credit card, debit card, supermarket loyalty card, or

frequent flyer card, or apply for any of the above

You surf the Web, reply to an Internet newsgroup, subscribe to a magazine, rent a video, join a club, fill out a contest entry form,

You pay for prescription drugs, or present you medical care number when visiting the doctor

Collection of personal data may be beneficial for companies and consumers, there is also potential for misuse



Protect Privacy and Data Security

Fair information practices

International guidelines for data privacy protection

Cover aspects relating to data collection, purpose, use, quality, openness, individual participation, and accountability

Purpose specification and use limitation

Openness: Individuals have the right to know what information is collected about them, who has access to the data, and how the data are being used

Develop and use data security-enhancing techniques

Blind signatures

Biometric encryption

Anonymous databases






prototypes




Summary



Trends in Data Mining (1)

Application exploration

development of application-specific data mining system

Invisible data mining (mining as built-in function)

Scalable data mining methods

Constraint-based mining: use of constraints to guide data mining systems in their search for interesting patterns

Integration of data mining with database systems, data warehouse systems, and Web database systems



Trends in Data Mining (2)

Standardization of data mining language

A standard will facilitate systematic development, improve interoperability, and promote the education and use of data mining systems in industry and society

Visual data mining

New methods for mining complex types of data

More research is required towards the integration of data mining methods with existing data analysis techniques for the complex types of data

Web mining

Privacy protection and information security in data mining






prototypes




Summary



Summary

Domain-specific applications include biomedicine (DNA), finance, retail and telecommunication data mining

There exist some data mining systems and it is important to know their power and limitations

Visual data mining include data visualization, mining result visualization, mining process visualization and interactive visual mining

There are many other scientific and statistical data mining methods developed but not covered in this book

Also, it is important to study theoretical foundations of data mining

Intelligent query answering can be integrated with mining

It is important to watch privacy and security issues in data mining



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http://www.cs.sfu.ca/~han/dmbook

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